Conversion of object-related traffic sensor information at roadways and intersections for virtual dynamic digital representation of objects

ABSTRACT

A platform for visualization of traffic information at an observed roadway or traffic intersection converts data collected from sensors for rendering as dynamic animations on a virtual map of the observed roadway or traffic intersection. The platform parses and curates incoming sensor data from either a single or multiple sensors representing one or more objects at the observed roadway or traffic intersection, and translates at least location data of each object for correlation of the object&#39;s movement relative to the observed roadway or traffic intersection. The platform then generates dynamic animations of the movement of each object and displays the animations as an overlay on the virtual map.

CROSS-REFERENCE TO RELATED PATENT APPLICATION(S)

This patent application claims priority to, and is a continuation of,U.S. non-provisional application Ser. No. 17/216,021, filed on Mar. 29,2021, the contents of which are incorporated in their entirety herein.In accordance with 37 C.F.R. § 1.76, a claim of priority is included inan Application Data Sheet filed concurrently herewith.

FIELD OF THE INVENTION

The present invention relates generally to the field of trafficmanagement. More specifically, the present invention relates to systemsand methods of enhanced traffic observation and monitoring, by capturinginformation from a single or multiple sensors, fusing the informationtogether, and outputting a combined view and animation of theinformation for improved viewing, interpretation and analysis ofintersection usage and movement.

BACKGROUND OF THE INVENTION

Since the 1930s, sensors have been used to detect vehicles on roadwaysand at intersections, and this information has been utilized in variousways to manage traffic flow. Most commonly, sensor information has beenused locally at a particular intersection to provide efficient actuationof the traffic signal at that intersection. Sensors and traffic flowmanagement have evolved in the decades since, and with the advent ofnewer detection systems such as video and radar, sensors became able todetect and enable classification of different types of vehicles and roadusers. Use of such sensors are now standard practice to actuate trafficsignals to provide indication of a red light or a green light forapproaching vehicles at intersections. Information collected from suchsensors is also regularly used for counting of the different vehiclesand roadway users, and can be used by cities, counties and states toidentify road usage, find traffic anomalies, and plan for future trafficflow.

Radar systems are commonly used to monitor traffic and provideinformation to traffic signal controllers to actuate the traffic signalsat the different approaches. Additionally, images collected from videodetection cameras are often viewable at a remote or central site toallow traffic engineers to see what is occurring at a particularintersection. For example, some traffic control agencies employ aremotely-movable camera that can be steered to view different areas ofthe intersection, and having pan, tilt, and zoom functions, to enabledifferent views, angles, and focal lengths at such locations.

Despite advances in traffic detection systems such as video and radar,traffic engineers and those involved in transportation planning remaininterested in real-time information for all traffic within theirjurisdiction, and being able to view such real-time information in amanner that enhances their ability to carry out transportation policyand monitor and manage traffic flows on busy roadways. Existingtechnology however lacks an approach that allows the traffic monitoringand control community to visualize traffic information and flow byviewing all of the traffic at each intersection on a real-time or nearreal-time basis.

Modern approaches such as ATMS (Advanced Traffic Management Systems)attempt to do this by providing information that is presented as adigitized overlay on a map. This information includes a trafficcontroller's signal state (such as red, yellow, green) as well as timinginformation, and information about crosswalk actuation and equipmentstatus. Such systems lack detail on type, location and movement ofspecific roadways users present at an intersection.

Accordingly, there is a need in the existing art for improvements inmonitoring of traffic information. There is a further need in theexisting for approaches to visually presenting details of roadway userstogether with other relevant information about a roadway or intersectionfor traffic management. Still further, there is a need in the existingart for processing traffic data collected from multiple sensors,curating such data for presentation to a user, and translating such datainto a format that enables real-time visualization on a displayinterface.

BRIEF SUMMARY OF THE INVENTION

The present invention provides a framework for presenting informationcollected by traffic sensors, in one or more systems and methods forcreating dynamic digital representations of roadway uses identified insensor data for display as an overlay on a digitized intersection orroadway map for human viewing on a display interface. This framework isembodied in a traffic visualization platform that processes sensor datain a traffic detection area on an observed roadway or at or near anobserved traffic intersection to identify characteristics of specificobjects in the sensor data, curates this processed data to identifymissing and erroneous information, and translates informationrepresenting each object to derive location data that is then convertedinto real-time geospatial coordinates that are relative to images of theobserved roadway or traffic intersection. The platform then createsdigital representations of roadway users, and generates dynamicanimations of this information for display as an overlay on a map of theobserved roadway or traffic intersection for human viewing, togetherwith other relevant traffic and/or signal information.

It is therefore one objective of the present invention to provide asystem and method for collecting data relating to multiple objects andrepresenting different roadway users present at a roadway or trafficintersection from different types of sensors, and visually representingthe multiple objects for improvements in traffic management. It isanother objective of the present invention to parse the data fromdifferent types of sensors to identify different types objects, anddiscern their location and movement relative to an observed roadway ortraffic intersection. It is still another objective to curate the datafrom the different types of sensors to identify missing and erroneousinformation. It is a further objective to derive location data for eachobject, and represent the location as real-time geospatial coordinatesrelative to the observed roadway intersection, in preparation fordigitized representations of the objects.

It is still a further objective of the present invention to provide asystem and method of presenting dynamic animations of these roadwayusers and their type, location, and motion, and as an overlay on a mapof the observed roadway or traffic intersection. It is yet anotherobjective of the present invention to provide these dynamic animationsas digital representations of real-time activity at the observed roadwayor traffic intersection on a display interface for human viewing, toenable improvements in traffic management. It is yet a further object ofthe present invention to provide systems and methods of applying suchdigital representations of real-time activity at the observed roadway ortraffic intersection to generate outputs to a traffic signal controllerto aid in operational efficiency and traffic management, for example toadjust phase cycle times.

Other objects, embodiments, features and advantages of the presentinvention will become apparent from the following description of theembodiments, taken together with the accompanying drawings, whichillustrate, by way of example, the principles of the invention.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate several embodiments of theinvention and together with the description, serve to explain theprinciples of the invention.

FIGS. 1A-1B are a systemic diagram illustrating elements of a trafficvisualization platform according to the present invention;

FIG. 2 is a flowchart of steps in a process of performing the trafficvisualization platform according to one embodiment of the presentinvention; and

FIG. 3 is a further chart outlining inputs and functions in a trafficvisualization platform according to the present invention.

DETAILED DESCRIPTION OF THE INVENTION

In the following description of the present invention, reference is madeto the exemplary embodiments illustrating the principles of the presentinvention and how it is practiced. Other embodiments will be utilized topractice the present invention and structural and functional changeswill be made thereto without departing from the scope of the presentinvention.

FIG. 1A and FIG. 1B together show a system diagram of a trafficvisualization platform 100 for representation of objects 101 and othertraffic data elements 102 in a transportation environment 103, such asan observed roadway 104 or traffic intersection 105, by a trafficdetection system 106. Such a traffic visualization platform 100 isconfigured to create a dynamic animation 176 of objects 101 as anoverlay 176 on a digitized map 172 of the transportation environment103, for use in a traffic management system 108 to at least realizeimprovements in traffic intersection control and public safety.

In such a traffic visualization platform 100, input data 110 in the formof sensor data 112 and captured by one or more types of sensors 120 isprocessed to detect, identify, and render objects 101 for digital,virtual representation 174 thereof on a display interface 192. Thetraffic visualization platform 100 is performed within one or moresystems and/or methods that includes several components, each of whichdefine distinct activities and functions for processing input data 110from the different types of sensors 120, and for accuratelycharacterizing attributes of the objects 101.

The traffic visualization platform 100 ingests, receives, requests, orotherwise obtains input data 110 that represents observations ofactivity in the transportation environment 103, such as an observedroadway 104 or observed traffic intersection 105, as noted above. Inputdata 110 is obtained from one or more sensors 120 that are part of atraffic detection system(s) 108. Such sensors 120 may be positioned inor near a roadway 104 or traffic intersection 105, for example proximateto a traffic signal controller, and may include imaging systems 121 suchas cameras (including RGB, video, or thermal cameras), radar systems122, magnetometers 123, acoustic sensors 124, loops 125, ultrasonicsensors 126, piezoelectric sensors 127, air pressure tubes 128, and anyother sensors, devices or systems 129 which are capable of detecting apresence of objects 101 within a transportation environment 103. Forexample, sensors 120 may further include light-based (such asultraviolet, visible, or infrared light) or laser-based sensing systems,such as LiDAR. It is to be understood that any combination of suchsensors 120 may be used to detect objects 101 within a traffic detectionsystem 108.

Input data 110 may also other types of traffic data elements 102 thatrepresent traffic or object-related information pertaining to anobserved roadway 104 or observed traffic intersection 105, which may ormay not be provided by or derived from sensor data 112 collected by theone or more sensors 120. These other types of information may also beincluded in or together with the dynamic animation 176 as an overlay 174on a map 172 together with other digital, virtual representations, oricons 178, representing objects 101 and any other related information.For example, input data 110 may include speed data 114 for the roadway104 or traffic intersection 105, such as a posted speed limit, anaverage estimated speed, and a current estimated speed. This type ofinformation may be maintained and provided by a traffic signalcontroller 117, or supplied by 3^(rd) party providers, and may be theproduct of surveys, taken over time, of actual roadway usage.Alternatively, such speed data 114 may also be derived from the sensordata 112, (for example, an average estimated speed or current estimatedspeed for all vehicles for the observed roadway 104 or trafficintersection 105.)

Input data 110 may also include roadway and intersection data 116. Thismay include, for example, data provided by a traffic signal controller117 (such as information identifying the traffic signal controller 117and its location) and data related to the observed roadway 104, thetraffic intersection 105 itself, and any other approach thereto.Examples of data related to the observed roadway 104 or the trafficintersection 105 include the number lanes, the type of roadway 104 orintersection 105, the configuration of the intersection 105, thelatitude and longitude (positional coordinates) of the roadway 104 ortraffic intersection 105, and any other relevant geometric orgeographical information for the particular location.

Roadway and intersection data 116 may further include signal and phasecycle timing data 118, and any other information that may be useful fordigital, virtual representation as an overlay on a map of thetransportation environment in conjunction with a dynamic animation 176of objects 101 relative to the observed roadway 104 or trafficintersection 105.

Input data 110 collected from the traffic detection system 108 andsensors 120 is applied to a plurality of data processing elements 134 inthe traffic visualization platform 100 that are components within acomputing environment 130 that also includes one or more processors 132and a plurality of software and hardware components. The one or moreprocessors 132 and plurality of software and hardware components areconfigured to execute program instructions or routines to perform themathematical functions, algorithms, machine learning, and otheranalytical approaches comprising the data processing functions describedherein, and embodied within the plurality of data processing elements134.

The plurality of data processing elements 134 include a data ingest andinitialization element 140 that is configured to ingest, receive,request, or otherwise obtain the input data 110 as noted above, andinitialize the input data 110 for further processing within the trafficvisualization platform 100. The plurality of data processing elements134 also include a data preparation and curation element 150, configuredto execute one or more algorithms that parse 151 information in thesensor data 112 and curate 156 the parsed information to identifymissing and erroneous values among the input data 110. The plurality ofdata processing elements 134 further include a translation element 160that is configured to execute one or more algorithms to translatelocation and movement characteristics for each object 101 detected bysensors 120, and correlate such information with positional coordinatesrelative to the observed roadway 104 and traffic intersection 105, inpreparing for subsequent mapping and animation functions andpresentation of output data 180.

The plurality of data processing elements 134 may further include othercomponents, such as a mapping and animation element 170 that isconfigured to prepare translated input data 110 for display on aninterface 192 for users 109 of a traffic management system 108. Themapping animation element 170 executes one or more algorithms to performthe mapping, overlay, and animation functions, as well as creating iconsrepresenting roadway users based on the objects 101, for display on theinterface 192. Each of the maps 172, overlays 174, dynamic animations176, and icons 178 generated by the mapping and animation element 170may be prepared, for example, by packaging the derivative data intoappropriate packets for rendering on a display interface 192.

These data processing elements 134 are configured to generate outputdata 180 that may take many different forms, and which may or may not bepresented as part of information displayed to a user 109 on theinterface 192. Output data 180 may include a classification 181 of oneor more objects 101 detected by the traffic detection system 106, and acount 182 of each of the one or more objects 101, according to one ormore embodiments of the present invention. The output data 180 may alsoinclude an alarm 183, such as a verbal, visual, aural, or otherindicator on the display interface 192, for example to indicate that anincident has been detected by the traffic detection system 106 or towarn of various activities that can cause abnormal pedestrian andvehicle movements, such as prone objects or pedestrians that may havefallen to the pavement, or the presence of unauthorized vehicles in apedestrian area. Output data 180 may include specific, calculated objectcharacteristics, such as for example an object's speed 184 andtrajectory 185. Other outputs are also possible, such as an instructionto adjust or extend 186 traffic signal controller phase timing as aresult of objects 101 preset in the roadway 104 or traffic intersection105, or in response to those calculated characteristics of objects 101present at the roadway 104 or traffic intersection 105. Output data 180may include functions such as traffic analytics 187 and reporting 188.Output data 180 may be provided to one or more third party or externalapplications 189 for additional analytics and processing therein, suchas for example an external traffic management system or a particulartraffic signal controller.

The traffic visualization platform 100 of the present invention may alsoinclude a traffic management support tool 190, as discussed furtherherein, and such a tool 190 is one way that a user may view and interactwith the display interface 192 on which the animation, digitizedrepresentation, overlay, and mapping functions are executed forpresentation of information created therein to the user 109. Forexample, one or more of the maps, overlays, dynamic animations, andother digital representations may be recorded, stored in a database, andplayed back at a later time. It is to be understood however that suchfollow-on functions may or may not be executed through the managementsupport tool 190, and that therefore the user 192 may utilize otherapproaches to storing, recording, and playing back information on adisplay interface, as well as for performing other functions forprocessing data within the traffic visualization platform 100.

As noted above, the traffic visualization platform 100 includes a datapreparation and curation element 150, which is configured to process theinput data 110 by parsing 151 information in the sensor data 112, andcurating 156 the parsed information to identify missing and erroneousvalues among the input data 110, impute missing values, and remove ordelete erroneous, redundant, anomalous, stagnant, or otherwise unhelpfulvalues. Each of the parsing 151 and curating 156 aspects of the datapreparation and curation element 150 are performed by executing one ormore mathematical models or algorithms for specific functions therein.

The data preparation and curation element 150 performs a parsing 151 ofinformation in the sensor data 112 to derive certain characteristics ofthe objects 101. These characteristics may include identifying thesensor type 152 from which the data 112 was collected, and identifyingeach sensor's positional coordinates 153 (the sensor 120 capturing theobject 101, and any other sensors 120 at the observed roadway 104 ortraffic intersection 105), which are used in the translation element 160as discussed below to convert object information from native coordinatesof the particular sensor 120 used in the traffic detection system 106into geospatial or geolocation coordinates, such as for example GlobalPositioning System (GPS) coordinates, for determining each object'slocation and for eventual rendering as an overlay 176 on a map 178 ofthe roadway 104 or traffic intersection 105. The characteristicsidentified in the parsing function 151 also include an identificationand classification 154 of an object type, also as described furtherbelow. The data preparation and curation element 150 then generates aset of parsed information, that at least includes sensor data, sensorlocation data, and object type data.

The present invention is capable of identifying and classifying manydifferent types of objects 101. For example, the object 101 may be amotorized vehicle, such as passenger vehicle, a truck or othercommercial vehicle, a motorcycle, a motorized scooter, a wheelchair, abus, an emergency vehicle, or any other type of vehicle that is poweredby electrical or mechanical means. Additionally, the object 101 may be abicycle, a skateboard, a manually-powered scooter, a manually-poweredwheelchair, a baby carriage, a pedestrian, a pedestrian using a walkingaid, an animal, or a fallen object in the roadway or intersection, suchas for example a downed power pole or power line, or a fallen tree.Still further, the object 101 may be an incident occurring in theobserved roadway 104 or traffic intersection 105. It is to be understoodthat the traffic visualization platform 100 is capable of identifyingand classifying any type of object 101, and accordingly the presentinvention is not intended to be limited by any specific type of object101 listed herein.

The identification and classification 154 of an object type may beperformed by one or more algorithms that analyze contents of signalsgenerated by the one or more sensors 120. One approach is by performinga pixel analysis, in which one or more pixel attributes in signalsdetected by the one or more sensors relative to a traffic detection zonerepresenting an observed roadway 104 and traffic intersection 105 areevaluated to identify and classify an object 101 in the field of view ofthe traffic detection zone. Pixel attributes may be evaluated in manydifferent ways to perform the identification and classificationfunctions of the data preparation and curation element 150 according tothe present invention.

For example, the traffic visualization platform 100 may classify anobject 101 by associating groups of moving pixels having common pixelcharacteristics. This represents an analysis of the field of view todistinguish between foreground objects and background objects. Such ananalysis processes temporal information in the traffic detection zone toanalyze the foreground of the field of view, and processes spatialinformation to learn a detection zone background model. This temporalassociates data points, such as for example pixels, uses common datapoint characteristics and attempts to identify one or more groups ofmoving data points. Common data point characteristics enable an initialidentification of a group of moving data points as a foreground object,and these at least include a color, a luminance, a position, andmovement of the data points to identify an object in motion. The presentinvention may further determine if a group of moving pixels representone or more foreground objects inside the observed roadway 104 andtraffic intersection 105, and identify an object type of the one or moreforeground objects based on dominant object type features. Such featuresinclude pixel intensity, edges, texture content, shape, objectattributes, and object tracking attributes for each object type.

The detection zone background model may be applied to examine specificdata point attributes within the observed roadway 104 and trafficintersection 105, and attempt to adaptively learn what is in thebackground over time. The learned model is then applied to differentiateall of or a portion of data points in the traffic detection zone fromknown background objects. In this manner, the traffic visualizationplatform 100 extracts multi-dimensional spatial features, and thenlearns statistical thresholds for background characteristics, resultingin an adaptive model of the background that is continuously generatedand adjusted as additional sensor data 112 is ingested. Through thisprocess of analyzing spatial information, the traffic visualizationplatform 100 may continually learn what objects are part of thebackground to aid in the classification and identification 154 ofobjects 101 in the foreground. Background characteristics may includeone or more of a roadway surface, roadway or lane markings, and roadwayshadows within the observed roadway 104 and traffic intersection 105.These may include permanent and temporary characteristics as well asitems which change over time at different rates. Examples of themulti-dimensional data point attributes include a pixel histogram,directional edges, a gray scale mean, a motion analysis (optical flow),frame difference data, and corner features.

The data preparation and curation element 150 also performs a curation156 of the parsed information in the sensor data 112 to filter noisefrom the sensor data 112. This is carried out to identify 157 erroneous,missing, or stagnating data points within the identified characteristicsof each object 101. The traffic visualization platform 100 also imputes158 any data identified as missing from the parsed information, andremoves 159 erroneous data, to create a more viable set of objectinformation for translation in the translation element 160 andvisualization in the mapping and animation element 170.

The process of filtering or removing noise in the curation 156 functionof the data preparation and curation element 150 may be accomplishedusing several different approaches identify 157 and remove 159 erroneousdata points. For example, the traffic visualization platform 100 mayperform one or more statistical analyses on the input data 110 thatattempt to remove data points that are outside a statistical probabilityof occurrence. For example, if a suspected object appears 100+ timeswithin 10 seconds within a 5 square meter area, it is probably theresult of an error in the sensor 120, and may be rejected.Alternatively, curation 156 may include an analysis of boundaryconditions based on -pre-defined, normal characteristics of objects 101.In such a boundary analysis, where an object is smaller than or greaterthan the pre-defined boundary condition, the object 101 may beconsidered noise and therefore removed from the set of data points thatrepresent an object 101. A further alternative approach utilizes theobject's location; if the object's GPS coordinates (for example,computed using the translation element 160 as below) are inconsistentwith object location as determined from different directional views ordifferent viewing angles of other sensors 120, then the data points maybe considered erroneous and removed from a dataset. Such an approach maytherefore include evaluating data that is provided as feedback to thedata preparation and curation element 150 from the translation element160.

The data preparation and curation element 150 also evaluates data setsfor missing data, and imputes 158 missing information for each object101. This may also be accomplished using multiple approaches, such asfor example where analysis of an image suggests that an object 101 ispresent at a particular location, but certain information is missingfrom available data (such as, a field of view in a particular sensor isoccluded due to inclement weather) such that the GPS coordinates cannotbe fully confirmed, the present invention may apply a pixel analysis ofthe original image to evaluate an image and impute a location of anobject 101. Data as to object type 153 may also be imputed 159, forexample by application of a statistical analysis that analyzes aprobability that an object 101 is likely to be of a specific type basedon a comparison with existing or known information.

The translation element 160 of the present invention performs thefunctions of deriving 162 a location of each object 101 that representsits physical location converted into a set of global positioning system(GPS) coordinates relative to the observed roadway 104 or trafficintersection 105, based on the native coordinates of the particularsensor 120 used in the traffic detection system 106 to capture theobject 101 initially. This information may later be used to calculateand track movement 164 of each object 101 from one location to anotherwithin the field of view in the data preparation and curation elements150. This may be accomplished in one or more mathematical models andalgorithms configured to calculate a speed of a detected object 101 andits change in position over time based on the GPS coordinatesrepresenting the object's location.

Deriving 162 each object's location in a field of view of a sensor 120to discern its GPS coordinates may be performed by one or moremathematical models or algorithms that further analyze locationinformation of the one or more sensors 120. This may be accomplishedusing many different approaches, each of which begins with thegeospatial coordinates, such as Global Positioning System (GPS)coordinates provided by the GPS satellite navigation system, of thesensor 120 capturing the particular piece of input data 110 relative tothat object 101.

In one such approach, the translation element 160 derives 160 eachobject's 101 location and converts this information into real-time GPScoordinates, by relating the GPS coordinates of the acquiring sensor 120to GPS coordinates of other sensors 120 in the same roadway 104 ortraffic intersection 105, and confirm the position of each object 101.For example, the translation element 160 may acquire data relating to anobject 101 from three different sensors 120 in the observed roadway 104or traffic intersection 105, and triangulate data points therein toprovide at least an initial confirmation of the location of each object101 in terms of its GPS coordinates.

The translation element 160 then extrapolates the viewed location of theobject 101 based on a defined field of view of a selected sensor 120 inconjunction with the sensor's GPS coordinates, relative to either imageattributes or other representations of the observed roadway 104 ortraffic intersection 105, to convert the location of the object 101 intopositional coordinates and further confirm the object's spatialcharacteristics relative to the traffic environment 103. This also maybe accomplished in several different ways. In one embodiment, where theselected sensor 120 is a camera-based system 121, the translationelement 160 may extrapolate the viewed location of the object 101, andidentify that location, by analyzing sensor characteristics relative tothe field of view, such as for example the direction in which the camera120 is pointing, and at what angle the camera 120 is positioned,together with knowledge of the sensor's GPS coordinates, to arrive atGPS coordinates of an object 101.

Alternatively, in another embodiment, where the selected sensor 120 is acamera-based system 121, the translation element 160 may extrapolate theviewed location of the object 101, and identify its location, using apixel analysis of the image(s) captured by the camera-based system 121.One or more algorithms in such a pixel analysis that counts pixels todetermine position, using different formulas for each of the X, Y, Zdirections. By way of example, a count of 10 pixels may equate to 3meters in the Z direction, whereas a count of 10 pixels may equate to 10meters in the X and Y directions, and so forth. In other words,depending on the field of view, and different numbers of pixels mayequate to different distances relative to the X, Y and Z directions,from which the translation element 160 may determine the GPS coordinatesof an object 101.

In a further embodiment where the selected sensor 120 is a camera-basedsystem 121, the translation element 160 may extrapolate the location ofan object 101 based on an analysis of pixel intensity, and a rate ofchange thereof. For example, because an object 101 may be in motion andits position dynamic, velocity of an object 101 may be analyzed by arate of pixel change in the field of view of the camera-based sensor120.

In yet another embodiment where the selected sensor 120 is acamera-based system 121, the present invention may utilize apre-established reference point at the observed roadway 104 or trafficintersection 105 to define other geospatial points in the field of view.For example, the present invention may compare pixels representing theobject 101 in the field of view to pixels representing a stop bar at atraffic intersection 105, or lane markers in the observed roadway 104,and measure the number of pixels between the object 101 and referencepoint to establish positional coordinates of the object 101.

The translation element 160 may also extrapolate the viewed location ofthe object 101 where the acquiring sensor 120 is a radar system 121, inconjunction with GPS coordinates of the sensor. In this scenario, thetranslation element 160 may extrapolate the viewed location of theobject 101 by identifying the field of view of the radar system (as withthe camera system 120, the direction in which the radar system 121 ispropagating signals, and at what angle the radar system 121 ispositioned relative to the observed roadway 104 or traffic intersection1045), and then utilizes object data captured by the radar system 121 tocorrelate the object's position in the field of view with the radarsystem's positional coordinates to map the GPS coordinates of the object101.

Analyzing sensor characteristics relative to a field of view forderiving the location of an object 101 may be accomplished by applyingsensor awareness techniques that use devices or systems associated withthe sensors 120 to determine sensor characteristics for use as areference. For example, the framework 100 may discern the location ofthe sensor 120 using an onboard GPS system as reference. Also, theframework 100 may utilize a compass on board the sensor 120 to discernthe direction in which the sensor 120 is pointing.

The framework 100 may utilize one or more algorithms as noted above thatperform a pixel analysis of the image(s) captured by a sensor 120. Suchalgorithms apply a pixel-to-distance referencing framework to countpixels for determining position by applying formulas for each of the X,Y, Z directions. Such a framework performs two types of object locationestimation techniques: a manual technique, and an automatic technique.

In the manual technique, a user applies the pixel-to-distancereferencing framework to reference pixels in the X direction (e.g.,bottom edge) of the field of view, and correlates those pixels tophysical width distances (such as feet), for example by assuming thatthe bottom edge is 75 feet across. The user then applies thepixel-to-distance referencing framework to reference Y-direction pixelsto discern the depth of the field of view, for example by assuming thatthe distance in the Y-direction from bottom-to-top (and centered by theX-direction) refers to a specific depth, such as 800 feet in depth.Further, the user then applies pixel-to-distance referencing framework100 to assign a height of any object 101 in the field of view toreference the Z direction, to establish heights for all objects 101 inany depth.

The automatic technique of the pixel-to-distance referencing frameworkmay instead use attributes of commonly-known objects (e.g., car lengths,pedestrian height, traffic signage/apparatus, sun diameter, moondiameter) to establish pixel reference points in the X, Y and Zdirections. These may also be used to automatically adjust or tweak thepixel-to-real-world mapping performed in the manual technique.

The present invention may use either the manual technique or theautomatic technique for setup of the pixel mapping aspect, or to use theautomatic technique (e.g., tweaking) system to setup thepixel-to-distance referencing framework based on the user'sconfigurations.

Determining the GPS coordinates of the location of an object 101 byapplying a formula, such asObjectGPS=ConvertToGPS(ObjectXYPixel,SensorGPS)where ConvertToGPS is a function that uses the pixel-to-distanceframework above, and the location of the acquiring sensor (SensorGPS) todiscern object location. The outcome, ObjectGPS, is a represented as atuple comprised of [longitude, latitude].

The translation element 160 of the traffic visualization platform 100 isalso configured to prepare the parsed and curated information forvisualization on a display interface 192. The translation element 160executes this function in one or more mathematical models and/oralgorithms that may initially take location information for each object101 represented as real-time geospatial coordinates, such as GlobalPositioning System (GPS) coordinates, to calculate and track movement ofeach object 101 to ascertain motion data for each object 101.Ascertaining motion data involves calculating a speed and change inposition of each object 101, and this may also be accomplished in manydifferent ways. For example, one way to calculate the speed and changein position of an object 101 is by associating the object 101 with itsposition relative to trigger points identified within a field of view ofa sensor 120 to determine both a distance between one point and anotherpoint (spatial information), and also a time for the object 101 to passfrom one trigger point to another trigger point (temporal information).It is to be understood that many methods of calculating a speed andchange in position of an object to ascertain and track is movement arepossible, and within the scope of the present invention.

The translation element 160 then characterizes the motion data 164 ofeach object 101 as a series of locations relative to the reference pointfor the observed roadway 104 or traffic intersection 105. This isperformed by first identifying a native capture time of the sensor 120generating the information for each object 101, and then correlating theseries of locations to the native capture time. The translation element160 then sequences the series of locations by a time interval 166between different locations, for example based on the geospatial points,to define the motion of each object 101.

This time interval varies according to multiple factors. These factorsinclude the native capture time of the sensor, for example a frame rateof a video system, or a time of s signal captured from a radar system.Additionally, the translation element 160 may account for the capabilityof geospatial conversion processing algorithms to derive location datain real-time, as this may limit the speed at which objects 101 are ableto be displayed. Other factors are the speed of transmission, such asthe bandwidth of the communication system, and the computer or computerson which the information is displayed, each of which may also impact thespeed at which display of the object motion can occur within the trafficvisualization platform 100, and consequently, the time interval selectedand applied to define the motion of the object 101 on the displayinterface 192.

The outcome of the data parsing and curation element 150 and thetranslation element 160 is a set of derivative data representing one ormore objects 101 and other information of interest in an observedroadway 104 or traffic intersection 105. The mapping and animationelement 170 then takes this resulting derivative data, comprised ofconverted and correlated location data, and other relevant informationsuch as object type, and prepares it for display of rendered trafficinformation. The mapping and animation element 170 prepares informationfor display by initiating and/or creating a digitized map 172 of theobserved roadway 104 or traffic intersection 105. The map 172 mayrepresent the observed roadway 104 or traffic intersection 105 in manydifferent formats. For example, the map 172 may display a singleintersection, a single roadway, a single approach, or multipleintersections, roadways and approaches. The map 172 may also present theobserved roadway 104 or traffic intersection 105 in a combination ofsingle and multiple elements, for example by highlighting particularapproaches at a single intersection. The user 109 may customize views ofthe observed roadway 104 or traffic intersection 105 shown by map 172,for example using the traffic management support tool 190.

The digitized map 172 may be a natively-generated representation of theobserved roadway 104 or traffic intersection 105, or may be an image orrepresentation acquired by the by the traffic visualization platform 100from an external source, such as a provider of satellite imagery of thelocation of the observed roadway 104 or traffic intersection 105. It isto be understood that any type of digital representation of the observedroadway 104 or traffic intersection 105 may be utilized, and is withinthe scope of the present invention, and therefore the currentspecification and claims are not to be limited to any one type of map172 specifically referenced herein.

The mapping and animation element 170 also generates an overlay 174 formap 172 as a digitized representation of sensor data 112. This includesa representation of one or more objects 101 at the observed roadway 104or traffic intersection 105, and any information to the object 101. Theoverlay may further include other information that is not collected bysensor data 112, such as traffic signal information (for example,indicating a current phase cycle and phase cycle timing), public or masstransit information, such as roadway usage by such vehicles and scheduletimes relative to the observed roadway 104 or traffic intersection, andinformation about emergency vehicles in or near the observed roadway 104or traffic intersection 105. Many other types of information may beprepared and shown as part of the overlay 174, and it is to beunderstood that the present invention is not to be limited to particulartypes of information referenced herein.

The mapping and animation element 170 further generates a dynamicanimation 176 of the objects 101 for display on the display interface192 based on the converted location data from the translation element160. The dynamic animation is presented together with the overlay 174 onthe map 172 to indicate movement of the objects 101 at the observedroadway 104 or traffic intersection 105.

One or more icons 178 may be further created to represent variousroadway users, such as objects 101. Icons 178 may be presented in manydifferent ways on the display interface 192. For example, objects 101themselves may be represented icons 178. Additionally, users 194 may bepresented with a larger version of each icon 178, or a different icon178, for each object 101 when hovering a cursor over the object 101 onthe display interface 192. Icons 178 may also be used to represent thetraffic signals and roadway elements such as lanes, lane markings, andobstacles (such as for examples trees or signs) at the observed roadway104 or traffic intersection 105. Icons 178 may therefore be used todigitally represent any object 101 or feature of the observed roadway104 or traffic intersection 105, and any type of representation may beused as an icon 178.

Displayed objects 101 (and other relevant information for the user 109)may also be identified according to their importance to the user 109.Digital representations such as labels may therefore also be generatedby the traffic visualization platform 100, such as for example toindicate near-miss collisions, objects 101 traveling over the postedspeed limit or surveyed speed limit, and any other information desiredby the user 109.

As noted above, the traffic visualization platform 100 may include atraffic management support tool 190, and such a support tool 190 isconfigured to support the follow-on utility of the output data 180. Thetraffic management support tool 190 may be utilized by a user to observeclassifications 181 and counts 182 of objects 101 at an observed roadway104 or traffic intersection 105, any alarms 183 generated at theobserved roadway 104 or traffic intersection 105, and speeds 184 andtrajectories 185 of objects 101 therein. The traffic management supporttool 190 may be further utilized to perform activities such asgenerating traffic analytics 187 and generating and managing reports 188of the output data 180.

The traffic management support tool 190 may also be utilized to manageand monitor functions such as adjusting or extending 186 a trafficsignal controller at or near the observed roadway 104 or trafficintersection 105, and/or phase cycle timings of such a traffic signalcontroller. The traffic visualization platform 100 may therefore beconfigured to generate one or more signals or instructions for such atraffic signal controller in response to the output data 180.

The traffic management support tool 190 may also be utilized to generateand provide data to external systems 189. Such systems 189 may includeexternal traffic management systems and traffic signal controllersystems, for example as a signal to adjust or extend phase cycle timingsfor out-of-network traffic signal controllers that may be affected bytraffic conditions at the observed roadway 104 or traffic intersection105.

The traffic management support tool 190 may enable users 109 tocustomize how information is viewed on the display interface 192, andgenerate custom digital representations for display. As noted above,displayed objects 101 and other relevant information may be indicated bydigital representations, such as labels or icons 178, for example toindicate near-miss collisions, objects 101 traveling over the postedspeed limit or surveyed speed limit, etc. These digital representationsmay be customized by the user 109 using the traffic management supporttool 190.

The traffic management support tool 190 may include widgets, drop-downmenus, and other indicia presented via the display interface 192 thatenable a user to make selections and perform functions attendant tooperation of the traffic visualization 100, or customize the informationpresented therein. A user 109 may interact with the support tool 190 viaan application resident on a computing device and/or using a graphicaluser interface, and various settings of the traffic visualizationplatform 100 may be configured using the traffic management support tool190, such as for example the size, type and color of a map 172, icons178, or other digital representations. It is to be understood that manysettings are possible within the traffic visualization platform 100, andthe present invention is not to be limited to any one function of thetraffic management support tool 190 relative to adjusting settings orconfiguring functions of the platform 100.

As noted above, the maps 172, overlays 174, dynamic animations 176, andicons 178 or other digital representations may be recorded, stored in adatabase, and played back at a later time. Playback of the informationpresented on a display interface 192 may include a replay function thatdisplays historical information for a specified or defined time period.The playback speed may be varied from ‘slow’ to ‘fast’ in variousincrements, and both the period of time specified by the user, and speedof playback, are examples of customizations that may be directed by theuser 109.

Other types of information may also be displayed on the interface 192.For example, weather information may be ingested by the trafficvisualization platform 100 and displayed along with object 101information and other related information to show the user 109 theinteraction between traffic conditions such as, for example, visibletraffic flow, and the existing weather conditions at the observedroadway 104 and traffic intersection 105, such as fog, rain, hail, snow,etc., and roadway conditions such as the presence of ice.

The present invention also contemplates that many layers of applicationprogramming interfaces (APIs) may be utilized within the trafficvisualization platform 100, for example to enable ingest of particulartypes of input data 110, or for accessing or distributing customizeduses of the output data 180. One layer of APIs may be utilized toconnect incoming sensor data 112 with the traffic visualization platform100 itself. Different APIs may be provided for each type of sensor 120,as each sensor 120 may generate data having different formats, and mayrequire pre-processing to format the data to be analyzed. APIs may bemanaged by an API element 136 specifically configured to enable theAPIs, for example as a specific sub-module of the data ingest andinitialization element 140 for intake of certain types of informationthat require a particular format or conversion from a particular format.The data ingest and initialization element 140 may itself be thought ofas a layer of APIs configured to ingest and initialize input data 110.

A further layer of APIs may be provided for output data 180. One or moreAPIs may be developed to enable the follow-on forms of the output data180 as discussed above. Third parties, for example, may utilize suchAPIs to develop their own, follow-on uses of the output data 180, suchas to customize alarms, analytics, reports, recommendations, or signalsor instructions provided third-party or external systems 189. APIs mayalso be provided to enable customized interfaces via the trafficmanagement support tool 190 for visualizing information with the trafficvisualization platform 100.

Communications within the traffic visualization platform 100 may includeboth wired and wireless methods of transmitting data. For example, thetraffic detection system 106 may transmit data using either wiredconnections, such as from sensors to traffic controller cabinets orother locations proximate to roadways 104 or traffic intersections 105,or via wireless signals. Similarly, information may be transmitted fromtraffic controller cabinets or other locations proximate to roadways 104or traffic intersections 105 (or from traffic detection systems 106directly) using either wired connections or wireless signals. Any typeof wireless communications protocols may be utilized in the trafficvisualization platform 100, such as cellular networks, Bluetoothconnections, Wi-Fi (wireless local area networking) connection, DSRC(dedicated short-range communications), NFC (near-filed communications)or any other form of wireless transmission.

FIG. 2 is a flowchart illustrating steps in a process 200 for performingthe traffic visualization platform 100, according to one or moreembodiments of the present invention. Such a process 200 may include, asnoted above one or more functions, mathematical models, algorithms,machine learning processes, and data processing techniques for the dataprocessing elements 134 within such a platform 100, and for the variousfunctions of each element 134.

The process 200 is initialized at step 210 by ingesting input data 110collected by sensors 120 and representing one or more objects 101captured within a field of view at or near an observed roadway 104 ortraffic intersection 105. This information is communicated to the datapreparation and curation element 150 to perform the parsing 151 andcuration 156 functions. The process 200 therefore continues by derivingcharacteristics of each object 101 at step 220, such as for example anidentifying object type, the type of sensor 220 generating the inputdata 110 for each object 101, and of the geospatial coordinates (such asGPS coordinates) of the capturing sensor and all other sensors 120 atthe observed roadway 104 or traffic intersection 105. This step producesa set of parsed information which is then curated at step 230 to producea modified dataset, by identifying missing information for each object101, as well as information that is not useful, such as erroneous,anomalous, and stagnant information for each object 101. Missinginformation is imputed for each object 101 where possible, andinformation that is not useful is deleted from the set of parsedinformation.

The process 200 then passes the set of parsed information to thetranslation element 160 to translate the curated, modified dataset atstep 240, to begin preparing the object information for rendering on adisplay interface 192. At step 240, the process 200 derives spatialcharacteristics of each object 101, and converts this information intolocation data represented as real-time geospatial coordinates relativeto the field of view for each sensor 120 to define the location of eachobject 101. At step 250, the process 200 defines the fields of view foreach sensor 120 to relate the geospatial coordinates of the acquiring orcapturing sensor 120 to other sensors 120 at the observed roadway 104 ortraffic intersection 105.

At step 260, the object's location is converted into a set of GPS orother geospatial positional coordinates by triangulating data pointsrelative to the object's position in the fields of view of the othersensors 120 to confirm the position of each object 101, andextrapolating the viewed location of each object 101 based on thedefined field of view of the selected sensor 120, as described in detailabove. The process 200 may also characterize movement of each object 101to define motion data, as a series of locations relative to thereference point, based on the native capture time of the sensor 120 thatproduced the input data 110 for each object 101. This is performed, asnoted above, by identifying the native capture time of the sensor 120generating the information for each object 101, correlating the seriesof locations to the native capture time, and sequencing the series oflocations by a time interval between different locations to define themotion data for movement of each object 101.

At step 270 of the process 200, the mapping and animation element 170generates a digitized map 172 of the observed roadway 104 or trafficintersection 105, and animation data representing the type, location,and motion of each object 101 to be displayed. At step 280, the mappingand animation element 170 creates an overlay 174 of the animation dataonto the digitized map 174 as a dynamic animation 176 of activity at theroadway 104 or traffic intersection 105. At step 290, the process 200displays the dynamic animation 174 as an overlay 176 on the digitizedmap 172 on a display interface 192, together with all other relevantdata for that particular location, such as speed data 114, roadway andintersection data 116, information relating to a traffic signalcontroller 117, signal and phase cycle timing data 118, etc.

FIG. 3 is a further flowchart outlining data flow of inputs tohigh-level functions within the traffic visualization platform 100,according to the present invention. Blocks 310 and 320 representincoming sensor data 112 and other types of input data 110, such as forexample roadway and intersection data 116 and traffic signal controllerdata 117. Sensor data 112 may be provided as a stream of information,represented for example as (where the sensor 120 is a camera-basedimaging system 121)

Cam1,06052019 12:03:54.325,Bike,021,100,120

|Sensor|-----------Date&Time---|Type|Sensor obj location|

The roadway intersection data 116 and traffic signal controller data117, and any other relevant information not collected by sensors 120,may be stored locally at a traffic signal controller in operation at theroadway 104 or traffic intersection 105, or stored in a central locationor in a cloud computing environment or system.

This incoming information is then provided to block 330 for parsing andcuration of the sensor information, and then to block 340 for conversionof the parsed and curated sensor information into geospatialcoordinates, as discussed in detail above. The resultinggeospatially-converted data stream element may be represented forexample as:

-   -   Obj123,06052019 12:03:54.325,Ped,122.456375,55.348723    -   |Obj ID|-------------Date&Time--|Type|Latitude/Longitude|

At block 350, the converted data stream is then displayed on aninterface 192 as an overlay 174 on a map 172, where one or more elementsof the data stream or presented as a dynamic animation 176 of objects101 detected within the traffic detection system 106.

It is to be understood that the geospatial coordinates may be providedfor sensors 120 relative any existing satellite navigation system, andthat Global Positioning System (GPS) coordinates provided by the GPSsatellite navigation system are but one type of coordinate system thatmay be utilized. Similarly, the present invention may convert locationdata into any type of geospatial coordinates, relative to any type ofsatellite navigation system, and therefore the present invention is notintended to be limited to any one type of coordinate system or relativeto any one type of satellite navigation system referenced herein.

The systems and methods of the present invention may be implemented inmany different computing environments 130. For example, they may beimplemented in conjunction with a special purpose computer, a programmedmicroprocessor or microcontroller and peripheral integrated circuitelement(s), an ASIC or other integrated circuit, a digital signalprocessor, electronic or logic circuitry such as discrete elementcircuit, a programmable logic device or gate array such as a PLD, PLA,FPGA, PAL, GPU and any comparable means. Still further, the presentinvention may be implemented in cloud-based data processingenvironments, and where one or more types of servers are used to processlarge amounts of data, and using processing components such as CPUs,GPUs, TPUs, and other similar hardware. In general, any means ofimplementing the methodology illustrated herein can be used to implementthe various aspects of the present invention. Exemplary hardware thatcan be used for the present invention includes computers, handhelddevices, telephones (e.g., cellular, Internet enabled, digital, analog,hybrids, and others), and other such hardware. Some of these devicesinclude processors (e.g., a single or multiple microprocessors orgeneral processing units), memory, nonvolatile storage, input devices,and output devices. Furthermore, alternative software implementationsincluding, but not limited to, distributed processing, parallelprocessing, or virtual machine processing can also be configured toperform the methods described herein.

The systems and methods of the present invention may also be wholly orpartially implemented in software that can be stored on a non-transitorycomputer-readable storage medium, executed on programmed general-purposecomputer with the cooperation of a controller and memory, a specialpurpose computer, a microprocessor, or the like. In these instances, thesystems and methods of this invention can be implemented as a programembedded on a mobile device or personal computer through such mediums asan applet, JAVA® or CGI script, as a resource residing on one or moreservers or computer workstations, as a routine embedded in a dedicatedmeasurement system, system component, or the like. The system can alsobe implemented by physically incorporating the system and/or method intoa software and/or hardware system.

Additionally, the data processing functions disclosed herein may beperformed by one or more program instructions stored in or executed bysuch memory, and further may be performed by one or more modulesconfigured to carry out those program instructions. Modules are intendedto refer to any known or later developed hardware, software, firmware,machine learning, artificial intelligence, fuzzy logic, expert system orcombination of hardware and software that is capable of performing thedata processing functionality described herein.

The foregoing descriptions of embodiments of the present invention havebeen presented for the purposes of illustration and description. It isnot intended to be exhaustive or to limit the invention to the preciseforms disclosed. Accordingly, many alterations, modifications andvariations are possible in light of the above teachings, may be made bythose having ordinary skill in the art without departing from the spiritand scope of the invention. It is therefore intended that the scope ofthe invention be limited not by this detailed description. For example,notwithstanding the fact that the elements of a claim are set forthbelow in a certain combination, it must be expressly understood that theinvention includes other combinations of fewer, more or differentelements, which are disclosed in above even when not initially claimedin such combinations.

The words used in this specification to describe the invention and itsvarious embodiments are to be understood not only in the sense of theircommonly defined meanings, but to include by special definition in thisspecification structure, material or acts beyond the scope of thecommonly defined meanings. Thus if an element can be understood in thecontext of this specification as including more than one meaning, thenits use in a claim must be understood as being generic to all possiblemeanings supported by the specification and by the word itself.

The definitions of the words or elements of the following claims are,therefore, defined in this specification to include not only thecombination of elements which are literally set forth, but allequivalent structure, material or acts for performing substantially thesame function in substantially the same way to obtain substantially thesame result. In this sense it is therefore contemplated that anequivalent substitution of two or more elements may be made for any oneof the elements in the claims below or that a single element may besubstituted for two or more elements in a claim. Although elements maybe described above as acting in certain combinations and even initiallyclaimed as such, it is to be expressly understood that one or moreelements from a claimed combination can in some cases be excised fromthe combination and that the claimed combination may be directed to asub-combination or variation of a sub-combination.

Insubstantial changes from the claimed subject matter as viewed by aperson with ordinary skill in the art, now known or later devised, areexpressly contemplated as being equivalently within the scope of theclaims. Therefore, obvious substitutions now or later known to one withordinary skill in the art are defined to be within the scope of thedefined elements.

The claims are thus to be understood to include what is specificallyillustrated and described above, what is conceptually equivalent, whatcan be obviously substituted and also what essentially incorporates theessential idea of the invention.

The invention claimed is:
 1. A method, comprising: receiving, as inputdata, information collected by a traffic detection system comprised ofone or more sensors, and representing one or more specific objectswithin a traffic environment; analyzing the input data in a plurality ofdata processing elements within a computing environment that includesone or more processors and at least one computer-readable non-transitorystorage medium having program instructions stored therein which, whenexecuted by the one or more processors, cause the one or more processorsto execute the plurality of data processing elements to generate digitalrepresentations of the one or more specific objects for viewing as adynamic animation on a display interface, by: parsing the informationcollected by the traffic detection system to identify characteristics ofthe one or more specific objects and the traffic detection system, thecharacteristics including a type of each object in the one or morespecific objects, a type of sensor capturing each object, and positionalcoordinates of the sensor capturing each object, to generate a set ofparsed information, curating the set of parsed information to identifyand remove missing and erroneous characteristics of each object,deriving location data for each object relative to the trafficenvironment, by extrapolating a viewed location of each object from afield of view of the type of sensor capturing each object,characterizing the location data by calculating a speed and change inlocation of each object relative to the traffic environment, byassociating each object with its location relative to one or morereference points identified within the field of view to ascertain bothspatial and temporal changes in the location data for each object, andconverting the location data into real-time geospatial coordinates; andgenerating object animation data representing the digital representationof the type of each object and the location data of each object fordisplay on a map of the traffic environment.
 2. The method of claim 1,further comprising overlaying the object animation data onto the map forvisualization of the information collected by the traffic detectionsystem as a dynamic animation of the traffic environment on the displayinterface.
 3. The method of claim 1, wherein the information collectedby a traffic detection system is sensor data collected by one or moresensors.
 4. The method of claim 3, wherein the one or more sensorsinclude at least one of an imaging system, a radar system, a loopsensor, a magnetometer, a piezo sensor, an acoustic sensor, andultrasonic sensor, and an air pressure sensor.
 5. The method of claim 1,wherein the parsing the information collected by the traffic detectionsystem to identify characteristics of the one or more specific objectsfurther comprises filtering the information collected from the trafficdetection system according to a type of sensor, and identifying a nativeframe rate of each type of sensor.
 6. The method of claim 1, wherein theparsing the information collected by the traffic detection system toidentify characteristics of the one or more specific objects furthercomprises identifying the type of sensor capturing each object, andidentifying the positional coordinates of the sensor capturing eachobject and positional coordinates of each type of sensor in the trafficdetection system.
 7. The method of claim 1, further comprisingclassifying each object as one or more of a bicycle, a motorcycle, atruck, a passenger vehicle, a commercial vehicle, a pedestrian, and anincident to determine the object type.
 8. The method of claim 1, whereinthe characterizing the location data further comprises tracking atrajectory of each object by identifying a series of locations relativeto the reference point, identifying a native capture time of the sensorgenerating the information for each object, correlating the series oflocations to the native capture time, and sequencing the series oflocations by a time interval between different locations based on thegeospatial coordinates.
 9. The method of claim 1, further comprisinggenerating one or more icons on the dynamic animation of the trafficenvironment depicting the one or more the specific objects.
 10. Themethod of claim 1, wherein the traffic environment is at least one of asignalized intersection, a roadway, a bicycle path, a pedestrian path,and a highway.
 11. A system for visually representing traffic objects ata traffic environment on a display interface, comprising: a datacollection element configured to receive input data comprised ofinformation collected by a traffic detection system comprised of one ormore sensors and representing one or more specific objects within atraffic environment; a data preparation and curation element configuredto parse the information collected by the traffic detection system toidentify characteristics of the one or more specific objects and thetraffic detection system, the characteristics including a type of eachobject in the one or more specific objects, a type of sensor capturingeach object, and positional coordinates of the sensor capturing eachobject, to generate a set of parsed information and curate the set ofparsed information to identify missing and erroneous characteristics ofeach object; a translation element configured to derive location datafor each object relative to the traffic environment, by extrapolating aviewed location of each object from a field of view of the type ofsensor each object, characterizing the location data by calculating aspeed and change in location of each object relative to the trafficenvironment by associating each object with its location relative to oneor more reference points identified within the field of view toascertain both spatial and temporal changes in the location data foreach object, and converting the location data into real-time geospatialcoordinates; and a mapping and animation element, configured to generateobject animation data representing the digital representation of thetype of each object and the location data of each object for display ona map of the traffic environment.
 12. The system of claim 11, whereinthe mapping and animation element is further configured to overlay theobject animation data onto the map for visualization of the informationcollected by the traffic detection system as a dynamic animation of thetraffic environment on the display interface.
 13. The system of claim11, wherein the one or more sensors include at least one of an imagingsystem, a radar system, a loop sensor, a magnetometer, a piezo sensor,an acoustic sensor, and ultrasonic sensor, and an air pressure sensor.14. The system of claim 11, wherein the data preparation and curationelement is further configured to filter the information collected fromthe traffic detection system according to a type of sensor and identifya native frame rate of each type of sensor.
 15. The system of claim 11,wherein the data preparation and curation element is further configuredto identify the type of sensor capturing each object, and identify thepositional coordinates of the sensor capturing each object andpositional coordinates of each type of sensor in the traffic detectionsystem.
 16. The system of claim 11, wherein the data preparation andcuration element is further configured to classify each object as one ormore of a bicycle, a motorcycle, a truck, a passenger vehicle, acommercial vehicle, a pedestrian, and an incident to determine theobject type.
 17. The system of claim 11, wherein the translation elementis further configured to track a trajectory of each object byidentifying a series of locations relative to the reference point,identifying a native capture time of the sensor generating theinformation for each object, correlating the series of locations to thenative capture time, and sequencing the series of locations by a timeinterval between different locations based on the geospatialcoordinates.
 18. The system of claim 11, wherein the mapping andanimation element is further configured to generate one or more icons onthe dynamic animation of the traffic environment depicting the one ormore the specific objects.
 19. The system of claim 11, wherein thetraffic environment is at least one of a signalized intersection, aroadway, a bicycle path, a pedestrian path, and a highway.
 20. A methodof visually representing traffic objects at a traffic environment on adisplay interface, comprising: preparing input data collected by atraffic detection system comprised of one or more sensors andrepresenting one or more specific objects within a traffic environmentfor a digital representation of the one or more specific objects on adisplay interface, by a) deriving a plurality of characteristics of theone or more specific objects and the traffic detection system from theinput data to generate a set of parsed information, the plurality ofcharacteristics including a type of each object in the one or moreobjects, a type of sensor capturing each object, and positionalcoordinates of the sensor capturing each object, and b) curating the setof parsed information to identify missing and erroneous characteristicsof each object; translating a curated set of the parsed information toderive a location of each object, by extrapolating a viewed location ofeach object from a field of view of the type of sensor capturing eachobject, characterizing the location data by calculating a speed andchange in location of each object relative to the traffic environment byassociating each object with its location relative to one or morereference points identified within the field of view to ascertain bothspatial and temporal changes in the location data for each object, andconverting the location data into real-time geospatial coordinates; andgenerating object animation data representing the digital representationof the one or more objects comprised of the type of each object and thelocation data for display of the digital representation of the one ormore specific objects on the display interface.
 21. The method of claim20, further comprising overlaying the object animation data onto a mapfor visualization of the information collected by the traffic detectionsystem as a dynamic animation of the traffic environment.
 22. The methodof claim 20, wherein the one or more sensors include at least one of animaging system, a radar system, a loop sensor, a magnetometer, a piezosensor, an acoustic sensor, and ultrasonic sensor, and an air pressuresensor.
 23. The method of claim 20, wherein the preparing input datacollected by a traffic detection system further comprises filtering theinput data according to a type of sensor and identifying a native framerate of each type of sensor.
 24. The method of claim 20, wherein thepreparing input data collected by a traffic detection system furthercomprises identifying the type of sensor capturing each object, andidentifying the positional coordinates of the sensor capturing eachobject and positional coordinates of each type of sensor in the trafficdetection system.
 25. The method of claim 20, further comprisingclassifying each object as one or more of a bicycle, a motorcycle, atruck, a passenger vehicle, a commercial vehicle, a pedestrian, and anincident to determine the object type.
 26. The method of claim 20,wherein the translating a curated set of the parsed information furthercomprises tracking a trajectory of each object by identifying a seriesof locations relative to the reference point, identifying a nativecapture time of the sensor generating the information for each object,correlating the series of locations to the native capture time, andsequencing the series of locations by a time interval between differentlocations based on the geospatial coordinates.
 27. The method of claim20, further comprising generating one or more icons on the dynamicanimation of the traffic environment depicting the one or more thespecific objects.
 28. The method of claim 20, wherein the trafficenvironment is at least one of a signalized intersection, a roadway, abicycle path, a pedestrian path, and a highway.